LEADER 05139nam 22007935 450 001 9910299472803321 005 20250430230205.0 010 $a9783319026398 010 $a3319026399 024 7 $a10.1007/978-3-319-02639-8 035 $a(OCoLC)865008489 035 $a(MiFhGG)GVRL6YFW 035 $a(CKB)3710000000057933 035 $a(MiAaPQ)EBC1592632 035 $a(MiFhGG)9783319026398 035 $a(DE-He213)978-3-319-02639-8 035 $a(EXLCZ)993710000000057933 100 $a20131029d2014 u| 0 101 0 $aeng 135 $aurun|---uuuua 181 $ctxt 182 $cc 183 $acr 200 10$aAutonomic Nervous System Dynamics for Mood and Emotional-State Recognition $eSignificant Advances in Data Acquisition, Signal Processing and Classification /$fby Gaetano Valenza, Enzo Pasquale Scilingo 205 $a1st ed. 2014. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2014. 215 $a1 online resource (xix, 162 pages) $cillustrations (some color) 225 1 $aSeries in BioEngineering,$x2196-887X 300 $a"ISSN: 2196-8861." 300 $a"ISSN: 2196-887X (electronic)." 311 08$a9783319026381 311 08$a3319026380 320 $aIncludes bibliographical references and index. 327 $aEmotions and Mood States: Modeling, Elicitation, and Classification through Autonomic Patterns -- Gathering Data from the Autonomic Nervous System: Experimental Procedures and Wearable Monitoring Systems -- Methodology of Advanced Signal Processing and Modeling -- Experimental Evidences on Healthy Subjects and Bipolar Patients -- Discussion on mood and emotional-state recognition using the Autonomic Nervous System Dynamics -- Summary of the Book and Direction for Future Research. 330 $aThis monograph reports on advances in the measurement and study of autonomic nervous system (ANS) dynamics as a source of reliable and effective markers for mood state recognition and assessment of emotional responses. Its primary impact will be in affective computing and the application of emotion-recognition systems. Applicative studies of biosignals such as: electrocardiograms; electrodermal responses; respiration activity; gaze points; and pupil-size variation are covered in detail, and experimental results explain how to characterize the elicited affective levels and mood states pragmatically and accurately using the information thus extracted from the ANS. Nonlinear signal processing techniques play a crucial role in understanding the ANS physiology underlying superficially noticeable changes and provide important quantifiers of cardiovascular control dynamics. These have prognostic value in both healthy subjects and patients with mood disorders. Moreover, Autonomic Nervous System Dynamics for Mood and Emotional-State Recognition proposes a novel probabilistic approach based on the point-process theory in order to model and characterize the instantaneous ANS nonlinear dynamics providing a foundation from which machine ?understanding? of emotional response can be enhanced. Using mathematics and signal processing, this work also contributes to pragmatic issues such as emotional and mood-state modeling, elicitation, and non-invasive ANS monitoring. Throughout the text a critical review on the current state-of-the-art is reported, leading to the description of dedicated experimental protocols, novel and reliable mood models, and novel wearable systems able to perform ANS monitoring in a naturalistic environment. Biomedical engineers will find this book of interest, especially those concerned with nonlinear analysis, as will researchers and industrialtechnicians developing wearable systems and sensors for ANS monitoring. 410 0$aSeries in BioEngineering,$x2196-887X 606 $aBiomedical engineering 606 $aArtificial intelligence 606 $aPsychobiology 606 $aSignal processing 606 $aComputational intelligence 606 $aNeurosciences 606 $aBiomedical Engineering and Bioengineering 606 $aArtificial Intelligence 606 $aBiological Psychology 606 $aSignal, Speech and Image Processing 606 $aComputational Intelligence 606 $aNeuroscience 615 0$aBiomedical engineering. 615 0$aArtificial intelligence. 615 0$aPsychobiology. 615 0$aSignal processing. 615 0$aComputational intelligence. 615 0$aNeurosciences. 615 14$aBiomedical Engineering and Bioengineering. 615 24$aArtificial Intelligence. 615 24$aBiological Psychology. 615 24$aSignal, Speech and Image Processing. 615 24$aComputational Intelligence. 615 24$aNeuroscience. 676 $a168 700 $aValenza$b Gaetano$4aut$4http://id.loc.gov/vocabulary/relators/aut$0873960 702 $aScilingo$b E. Pasquale 801 0$bMiFhGG 801 1$bMiFhGG 906 $aBOOK 912 $a9910299472803321 996 $aAutonomic Nervous System Dynamics for Mood and Emotional-State Recognition$91951214 997 $aUNINA